In a manufacturing step of forming a minute pattern on a substrate, for example, when a semiconductor device or a liquid crystal apparatus is manufactured, to ensure a high yield, it is important to quickly locate a defect produced in a manufacturing step and take measures against the defect. In recent years, as semiconductor devices are increasingly miniaturized, even a minute defect can have a nonnegligible effect on the yield, and the number of types of defects to be observed has been increasing.
A SEM-based observation apparatus is used to observe such a variety of defects based on information on the position of a defect detected by an inspection apparatus that is typically a higher-level apparatus. Further, to identify a problematic manufacturing step, defects are classified on a defect type basis. The performance of observation apparatus has been dramatically improved, allowing observation of more minute defects. The improved performance along with improvement in throughput dramatically increases the number of acquirable observed images, which encourages development of a technology for automating classification of observed defects on a defect category basis (on a defect type basis) by using acquired images.
The function of automating defect classification is called ADC (automatic defect classification), and Patent Literature 1, for example, discloses a method for automatically classifying a defective portion by quantifying characteristics of the defect portion and using the quantified characteristic values.
Further, Patent Literature 2 discloses an invention that allows ADC results to be displayed in the form of confusion matrix.